Gene Expression Profiles Reveal Potential Targets for Breast Cancer Diagnosis and Treatment

Figuring out the molecular mechanisms underlying breast cancer is essential for the diagnosis and treatment of this invasive disorder. Hence it is important to identify the most significant genes correlated with molecular events and to study their interactions in order to identify breast cancer mechanisms. Here we focus on the gene expression profiles, which we have detected in breast cancer. High-throughput genomic innovations such as microarray have helped us understand the complex dynamics of multisystem diseases such as diabetes and cancer. We performed an analysis using microarray datasets by the Networkanalyst bioinformatics tool, based on a random effect model (REM). We achieved pivotal differential expressed genes like ADAMTS5, SCARA5, IGSF10, and C2orf40 that had the most down-regulation, and also COL10A1, COL11A1, and UHRF1 that they had the most up-regulation in four-stage of breast cancer. We used CentiScape and AllegroMCODE plugins in CytoScape software in order to figure out hub genes in the protein-protein interactions network. Besides, we utilized DAVID online software to find involved biological pathways and Gene ontology, also used Expression2kinase software in order to find upstream regulatory transcription factors and kinases. In conclusion, we have found that the statistical network inference approach is useful in gene prioritization and is capable of contributing to practical network signature discovery and providing insights into the mechanisms relevant to the disease. Our research has also identified novel transcription factors, kinases, pathways, and genes that may serve as important targets for the development of diagnostic biomarkers and treatments.